Typically, \( D \)  makes use of the forward operator \( A \)  to quantify the agreement between the reconstruction of \( x \)  and the measured data \( f \) .
\( R \)  may for instance represent the tv semi-norm [1]}, which is well known to favor piece-wise constant solutions.
For the sake of simplicity, we assume \( \eta \)  to be Gaussian, and consequently set \( D(x, f) = \frac{1}{2\sigma ^2} \left\Vert Ax - f\right\Vert ^2 \) , where \( \sigma ^2 \)  denotes the variance of \( \eta \) .
We discuss the choice of \( R \)  in the next section.
